德国汉诺威莱布尼兹大学2023年招聘博士后，具体情况以公告为准。原文如下：

汉诺威大学（Universität Hannover）全称为戈特弗里德·威廉·莱布尼茨汉诺威大学（Gottfried Wilhelm Leibniz Universität Hannover），是位于下萨克森州首府汉诺威的德国著名理工大学，始建于1831年，前身为“普鲁士皇家理工高校”（Königliche Technische Hochschule），是德国历史悠久的工业大学之一，现为德国九所顶尖理工大学联盟TU9成员之一，CESAER欧洲高等工程教育和研究大学会议联盟德国十所高校之一，T.I.M.E欧洲顶尖工业管理者高校联盟德国七所高校之一。

Research Engineer (m/f/d) in the Field of Machine Learning on Knowledge Graphs

Leibniz University Hannover

The Leibniz Joint Lab Data Science and Open Knowledge of the Technische Informationsbibliothek (TIB) and the L3S Research Center, under the supervision of Dr. Javad Chamanara and Prof. Dr. Sören Auer, invites applications for 2 positions of a Research Engineer (m/f/d) in the Field of Machine Learning on Knowledge Graphs (Salary Scale 13 TV-L, 100 %) to start at the next possible date. The positions are limited to 3 years with envisioned extension. There is the possibility to do a doctorate.

Scope of work

You will be placed in the context of the CLEVER project, which is an effort to bring AI/ML and Knowledge Graphs to the heart of cluster and edge computing. Your tasks will include the following:

Design, implement, train, optimize, test, and publish ML models for cluster resource and behavior prediction, based on property and/or knowledge graph techniques, e.g., embedding, link prediction, and link- property prediction

Define, design, and implement integration interfaces for the ML models and other components of the system (e.g., Kubernetes) using proper technologies e.g., library linking, RPC, gRPC, REST, etc.

Responsible for ensuring maintainable and scalable code for robustness and reliability

Write and maintain unit tests so that quality is maintained and adhered to functional requirements

Peer-reviewing code from team members

Participate in Product Backlog Refinement sessions to formulate user stories by clarifying the technical details

Develop demos, prototypes, and presentations

Appear and present in multi-party meetings or external events

Requirements

A requirement for employment is a successfully completed academic university degree (Master's degree or equivalent) in a relevant field of study such as computer science, mathematics or information science. Your competences should be grounded in computer science or related fields. You should be able to think creatively, grasp new knowledge quickly and combine abstract thinking with concrete problem solving, and be interested in tackling complex challenges.

Furthermore, we expect the following qualifications:

Proficiency in spoken and written English. Proficiency in German is a plus

5 years+ of work experience as a programmer

2 years+ of work experience as an ML developer (successfully delivered work should be presented)

Solid knowledge and experience with Python; familiarity with the GO language is an asset

Hands-on experiences with PyTorch or TensorFlow

Experience with AI/Ml on graphs including graph embedding techniques as well as link and property prediction

Experience with extracting data from RDBMS, RDF stores, and Web services

Strong experience in using REST APIs and interacting with them mainly via JSON

Familiarity with Docker, Dockerization, and related technologies

Familiarity with Kuberenetes

Analytical thinking, Creativity, teamwork, and communication skills

Passion to write clean code

Experience working with scrum methodologies is an advantage (but not a prerequisite)

Ability to write quality documentation, technical reports, and presentational material

Solid experience in using Git, Github, or Gitlab including, committing, pushing, branching, merging, pull requests, and issue management

What we offer

In the Joint Lab, you have the opportunity to further your scientific development in a dynamic and excellent research environment. We provide a scientifically and intellectually inspiring environment with an entrepreneurial mind set embedded in a leading technical university and one of the largest technical information centers being part of the Leibniz Association. There is close cooperation with the Research Center L3S of Leibniz Universität Hannover – the L3S is one of the world's leading research institutes in the fields of Web & Data Science. Last but not least, we attach great importance to an open and creative working atmosphere in which it is fun to work

Furthermore, we offer:

Funding for necessary equipment, conference and research visit travel

A modern workplace in a central location of Hannover with a collegial, attractive and versatile working environment

Flexible working hours (flexitime) as well as offers for reconciling work and family life, such as mobile work

An employer with a wide range of internal and external further education and training measures, workplace health promotion and a supplementary pension scheme for the public sector (VBL)

Discount for employees in the canteens of the Studentenwerk Hannover as well as the possibility to use the various offers of the University Sports Hannover

A salary according to the provisions of the collective agreement for the public service in Germany (TV-L)

The university aims to promote equality between women and men. For this purpose, the university strives to reduce under-representation in areas where a certain gender is under-represented. Women are under-represented in the salary scale of the advertised position. Therefore, qualified women are encouraged to apply. Moreover, we welcome applications from qualified men. Preference will be given to equally-qualified applicants with disabilities.

We look forward to receiving your application. In addition to your CV, please include a letter of motivation (including technology interests) with your application and send your application by February 27, 2023 in electronic form to

Email: matern@l3s.de

or alternatively via postal mail to:

Gottfried Wilhelm Leibniz Universität Hannover Joint Lab TIB / Forschungszentrum L3S z.Hd. Frau Simone Matern Welfengarten 1B 30167 Hannover

For further information, please contact Dr. Javad Chamanara (Email: chamanara@l3s.de).

More information on the Joint Lab can be found at: https: // www. tib.eu/en/research-development/joint-lab/

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